Detecting Changes in Mental Models during Interaction

Abstract

This paper introduces a novel computational cognitive model that maps latent mental models to observable behaviors, allowing the system to detect changes in users’ mental models from their actions. We propose an inference framework to dynamically adjust to the user’s evolving understanding and decision-making processes. An empirical experiment demonstrates the framework’s ability to accurately detect shifts in users’ mental models based on their interactions. The results indicate a consistent improvement in prediction accuracy and a decrease in variance over time, suggesting the model’s potential for real-time application in designing adaptive interactive systems.